IOT-Driven Precision Healthcare by Optimizing Brain Tumor Diagnosis with Machine Learning and Blockchain

Abstract

IoT - precision healthcare has transformed medical diagnostics. This makes intelligent decision-making possible as well as real-time monitoring and continuous data transmission plausible. Usually, conventional diagnosis methods reveal a rise in security flaws, delays, and mistakes in medical imaging data management. Blockchain technology and ideas from machine learning provide a creative approach to handle some issues. Analyzing the current developments in IoTs-driven precision healthcare helps one to grasp the possible contribution deep learning models could offer in improving the brain tumor classification. Many techniques of machine learning have shown excellent tumor detection accuracy. Among these approaches are support vectors machines (SVMs), convolutional neural networks (CNNs), and hybrid deep learning designs. Still, there are significant unresolved issues on privacy of data, interoperability, and the significant computational overhead. Among many different healthcare institutions, blockchain technology has great potential to support medical data encryption, patient privacy protection, and interoperability improvement. More especially, the survey centers on the advantages of blockchain-enabled security systems and artificial intelligence-powered imaging technologies. The method requires close reading research papers, clinical studies, and case reports released during the past five years with peer review under consideration. Regarding the identification of brain tumors, results show that operational efficiency, data security, and diagnostic accuracy are raised by a hybrid approach including blockchain technology, machine learning, and IoT. Research in future directions could be optimization of computational efficiency, development of federated learning-based models for distributed data training and increase of blockchain scalability for large-scale medical data management.